A large structural variant collection in Holstein cattle and associated database for variant discovery, characterization, and application.

Background Structural variants (SVs) such as deletions, duplications, and insertions are known to contribute to phenotypic variation but remain challenging to identify and genotype. A more complete, accessible, and assessable collection of SVs will assist efforts to study SV function in cattle and to incorporate SV genotyping into animal evaluation. Results In this work we produced a large and deeply characterized collection of SVs in Holstein cattle using two popular SV callers (Manta and Smoove) and publicly available Illumina whole-genome sequence (WGS) read sets from 310 samples (290 male, 20 female, mean 20X coverage). Manta and Smoove identified 31 K and 68 K SVs, respectively. In total the SVs cover 5% (Manta) and 6% (Smoove) of the reference genome, in contrast to the 1% impacted by SNPs and indels. SV genotypes from each caller were confirmed to accurately recapitulate animal relationships estimated using WGS SNP genotypes from the same dataset, with Manta genotypes outperforming Smoove, and deletions outperforming duplications. To support efforts to link the SVs to phenotypic variation, overlapping and tag SNPs were identified for each SV, using genotype sets extracted from the WGS results corresponding to two bovine SNP chips (BovineSNP50 and BovineHD). 9% (Manta) and 11% (Smoove) of the SVs were found to have overlapping BovineHD panel SNPs, while 21% (Manta) and 9% (Smoove) have BovineHD panel tag SNPs. A custom interactive database ( https://svdb-dc.pslab.ca ) containing the identified sequence variants with extensive annotations, gene feature information, and BAM file content for all SVs was created to enable the evaluation and prioritization of SVs for further study. Illustrative examples involving the genes POPDC3, ORM1, G2E3, FANCI, TFB1M, FOXC2, N4BP2, GSTA3, and COPA show how this resource can be used to find well-supported genic SVs, determine SV breakpoints, design genotyping approaches, and identify processed pseudogenes masquerading as deletions. Conclusions The resources developed through this study can be used to explore sequence variation in Holstein cattle and to develop strategies for studying SVs of interest. The lack of overlapping and tag SNPs from commonly used SNP chips for most of the SVs suggests that other genotyping approaches will be needed (for example direct genotyping) to understand their potential contributions to phenotype. The included SV genotype assessments point to challenges in characterizing SVs, especially duplications, using short-read data and support ongoing efforts to better characterize cattle genomes through long-read sequencing. Lastly, the identification of previously known functional SVs and additional CDS-overlapping SVs supports the phenotypic relevance of this dataset.